Last updated: 2018-08-30

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Unstaged changes:
    Modified:   NAMESPACE
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    Modified:   analysis/example_10x.Rmd
    Modified:   analysis/example_dropseq.Rmd
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    File Version Author Date Message
    html 0d53b7e tk382 2018-08-29 Build site.

Read Data : Yan

load('data/Yan.rda')
X         = as.matrix(yan)
genenames = rownames(X)
truelabel = as.character(ann$cell_type1)
numClust  = 6
rm(ann, yan)

Quality Control and Cell Filter

nGene = colSums(X > 0)
hist(nGene)

Expand here to see past versions of cell_filter-1.png:
Version Author Date
0d53b7e tk382 2018-08-29 

summaryX = cellFilter(X = X, 
                      genenames = rownames(X),
                      minGene = -Inf, 
                      maxGene = Inf, 
                      maxMitoProp = 0.1)
tmpX = summaryX$X
nUMI = summaryX$nUMI
nGene = summaryX$nGene
percent.mito = summaryX$percent.mito
det.rate = summaryX$det.rate

par(mfrow = c(1,4))
boxplot(nUMI, main='nUMI'); 
boxplot(nGene, main='nGene'); 
boxplot(percent.mito, main='mitochondrial gene', ylim=c(0,0.5)); 
boxplot(det.rate, main='detection rate', ylim=c(0,0.1))

Expand here to see past versions of cell_filter-2.png:
Version Author Date
0d53b7e tk382 2018-08-29

Gene filtering

X = X[rowSums(X) > 0, ]
genenames = genenames[rowSums(X) > 0]

#gene filter by dispersion
disp = dispersion(X, bins = 20)
plot(disp$z ~ disp$genemeans,
     xlab = "mean expression",
     ylab = "normalized dispersion")

Expand here to see past versions of basic_gene_filtering-1.png:
Version Author Date
0d53b7e tk382 2018-08-29 

select = which(abs(disp$z) > 1)
X = X[select, ]
genenames = genenames[select]

UMI Normalization

Use quantile-normalization to make the distribution of each cell the same.

nX = quantile_normalize(as.matrix(X))

Correct Detection Rate

After normalization, the linear relationship between the first PC and the detection rate usually disappears. The plots show that even without correction, the two are not heavily correlated. So we do not regress out the detection rate.

#take log
logX = as.matrix(log(nX + 1))

#check dependency
out = correct_detection_rate(logX, det.rate)

Expand here to see past versions of log_transform_and_det_correction-1.png:
Version Author Date
0d53b7e tk382 2018-08-29 

#regress out
log.cpm = out$residual
# log.cpm = logX

Dimension reduction and visualization.

pc = irlba(log.cpm, 20)
plot(pc$d, ylab = "singular values")

Expand here to see past versions of tsne-1.png:
Version Author Date
0d53b7e tk382 2018-08-29 

tsne = Rtsne(pc$v[,1:15], dims=2, perplexity = 10, pca=FALSE)
df = data.frame(tsne1 = tsne$Y[,1], tsne2 = tsne$Y[,2], truelabel = truelabel)
ggplot(df, aes(x=tsne1, y=tsne2, col = truelabel)) + geom_point()+
  ggtitle("True Label")

Expand here to see past versions of tsne-2.png:
Version Author Date
0d53b7e tk382 2018-08-29 

rm(pc)

Run the algorithm

Run SLSL on the log.cpm matrix.

out = SLSL(log.cpm, log=FALSE,
                filter = FALSE,
                correct_detection_rate = FALSE,
                klist = c(5,10,15),
                sigmalist = c(1,1.5,2),
                kernel_type = "combined",
                verbose=FALSE)
df$SLSL = as.factor(out$result)
ggplot(df, aes(x=tsne1, y=tsne2, col=SLSL))+geom_point()

Expand here to see past versions of slsl-1.png:
Version Author Date
0d53b7e tk382 2018-08-29 

adj.rand.index(out$result, as.numeric(as.factor(truelabel)))
[1] 0.8681222

Analyze

S = as.matrix(out$S)
palette.gr.marray <- colorRampPalette(c("ivory", "pink", "red", "brown"))(30)
labRow = rep("", 90)
labRow[c(3, 10,18, 35, 52, 76) ] = c("zygote",
                                    "2cell", "4cell", "8cell", "16cell",
                                    "blast")
heatmap.2(S,
          trace = "none",
          col = palette.gr.marray,
          Colv = F,
          Rowv = F, 
          rowsep = which(truelabel[1:89] != truelabel[2:90]),
          colsep = which(truelabel[1:89] != truelabel[2:90]),
          sepcolor = "gray",
          dendrogram = "none",
          labRow = labRow,
          labCol = labRow,
          key = F,
          breaks = seq(min(S), max(S),length=31),
          cexRow = 1,
          symbreaks = T)

Expand here to see past versions of analyze-1.png:
Version Author Date
0d53b7e tk382 2018-08-29

Session information

sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.5

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] parallel  stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] bindrcpp_0.2.2          gridExtra_2.3          
 [3] gdata_2.18.0            stargazer_5.2.2        
 [5] abind_1.4-5             broom_0.5.0            
 [7] gplots_3.0.1            diceR_0.5.1            
 [9] Rtsne_0.13              igraph_1.2.2           
[11] scatterplot3d_0.3-41    pracma_2.1.4           
[13] fossil_0.3.7            shapefiles_0.7         
[15] foreign_0.8-71          maps_3.3.0             
[17] sp_1.3-1                caret_6.0-80           
[19] lattice_0.20-35         reshape_0.8.7          
[21] dplyr_0.7.6             ggplot2_3.0.0          
[23] irlba_2.3.2             Matrix_1.2-14          
[25] quadprog_1.5-5          inline_0.3.15          
[27] matrixStats_0.54.0      SCNoisyClustering_0.1.0

loaded via a namespace (and not attached):
 [1] nlme_3.1-137              bitops_1.0-6             
 [3] lubridate_1.7.4           dimRed_0.1.0             
 [5] rprojroot_1.3-2           tools_3.5.1              
 [7] backports_1.1.2           R6_2.2.2                 
 [9] KernSmooth_2.23-15        rpart_4.1-13             
[11] lazyeval_0.2.1            colorspace_1.3-2         
[13] nnet_7.3-12               withr_2.1.2              
[15] tidyselect_0.2.4          compiler_3.5.1           
[17] git2r_0.23.0              labeling_0.3             
[19] caTools_1.17.1.1          scales_0.5.0             
[21] sfsmisc_1.1-2             DEoptimR_1.0-8           
[23] robustbase_0.93-2         stringr_1.3.1            
[25] digest_0.6.15             rmarkdown_1.10           
[27] R.utils_2.6.0             pkgconfig_2.0.1          
[29] htmltools_0.3.6           rlang_0.2.1              
[31] ddalpha_1.3.4             bindr_0.1.1              
[33] gtools_3.8.1              mclust_5.4.1             
[35] ModelMetrics_1.1.0        R.oo_1.22.0              
[37] magrittr_1.5              Rcpp_0.12.18             
[39] munsell_0.5.0             R.methodsS3_1.7.1        
[41] stringi_1.2.4             whisker_0.3-2            
[43] yaml_2.2.0                MASS_7.3-50              
[45] plyr_1.8.4                recipes_0.1.3            
[47] grid_3.5.1                pls_2.6-0                
[49] crayon_1.3.4              splines_3.5.1            
[51] knitr_1.20                pillar_1.3.0             
[53] reshape2_1.4.3            codetools_0.2-15         
[55] stats4_3.5.1              CVST_0.2-2               
[57] magic_1.5-8               glue_1.3.0               
[59] evaluate_0.11             RcppArmadillo_0.8.600.0.0
[61] data.table_1.11.4         foreach_1.4.4            
[63] gtable_0.2.0              purrr_0.2.5              
[65] tidyr_0.8.1               kernlab_0.9-26           
[67] assertthat_0.2.0          DRR_0.0.3                
[69] gower_0.1.2               prodlim_2018.04.18       
[71] class_7.3-14              survival_2.42-6          
[73] geometry_0.3-6            timeDate_3043.102        
[75] RcppRoll_0.3.0            tibble_1.4.2             
[77] iterators_1.0.10          workflowr_1.1.1          
[79] lava_1.6.2                ipred_0.9-6

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